Estimated US emergency room visit related to basketball (2017)

age	sex	n
14	male	34456.708
15	male	32565.140
16	male	31558.694
17	male	31300.324
13	male	28695.674
12	male	20628.453
18	male	19360.890
19	male	18137.962
20	male	15600.353
11	male	15425.811
21	male	13281.578
14	female	12503.775
13	female	12478.700
22	male	11176.448
10	male	10569.878
15	female	10059.873
23	male	9664.258
12	female	9389.971
24	male	9126.240
16	female	7578.840

---
title: "Estimated US emergency room visit related to basketball (2017)"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
injuries <- read_tsv("../data/NEISS/injuries.tsv")
population <- read_tsv("../data/NEISS/population.tsv")
products <- read_tsv("../data/NEISS/products.tsv")
```

### Basketball related ER visits {data-width=650}

```{r}
basketball_injuries_demographics <- injuries |>  filter(prod_code == 1205) |>
  count(age, sex, wt = weight, sort = TRUE)
basketball_injuries_demographics |>  ggplot(aes(x = age, y = n, color = sex)) +
  geom_line() +
  labs(title = "Basketball related ER Visits (2017)",
       y = "Estimated Number of Injuries")
```

### Top 20 demographic groups for basketball related ER visits (2017) {data-width=350}

```{r}
knitr::kable(basketball_injuries_demographics[1:20,c("age", "sex", "n")])
```